GEO ROI Models

1. Why traditional ROI models fail in generative search

In generative search, a click is no longer the expected outcome of every query. Traditional ROI models become incomplete when they treat that click as the sole connection between search activity and business value.

Zero-click Search explains the underlying behavior. A Pew Research Center panel study found that users clicked a result link in about 8% of visits with an AI summary, compared with about 15% without one. They clicked a link within the summary itself only about 1% of the time. An independent correlational analysis by Ahrefs estimated that an AI Overview reduced the click-through rate for the top-ranking page by about 34.5%.

Broader adoption data points in the same direction. Gartner’s February 2024 forecast projected a 25% decline in traditional search engine volume by 2026. BrightEdge’s one-year AIO study found that AI Overviews appeared on roughly half of all searches. Similarweb’s 2025 generative AI report reported a 76% year-over-year increase in monthly visits to generative AI services and more than 1.1 billion referral visits in June 2025 alone, a 357% year-over-year increase.

Three assumptions in standard SEO ROI math no longer hold under those conditions:

1. Every answer a user consumes produces a measurable session. Most do not. The answer itself may be the entire session.
2. Referrer headers identify the source. Referrer behavior for ChatGPT, Claude, and Perplexity varies by client, mode, and month. AI Search Attribution explains UTM failures, server-log fallbacks, and the difference between last-touch and assisted attribution.
3. The buyer’s first contact happens on your website. The AI answer is increasingly the first contact. The first session recorded in your analytics may be a direct visit after the buyer searches for your brand by name.

Tracking conversions from AI referrals alone may undervalue GEO by an estimated 5–10× because most of the influence comes from mentions that occur without a session and remain outside standard analytics. GEO Metrics defines the relevant KPIs; the models below translate them into business value.

A note on the term “GEO ROI Models.” This is a GEO Wiki working synthesis, not the name of an industry-standard framework. BrightEdge, Conductor, and Semrush offer vendor ROI calculators, but their proprietary single-number scores use formulas that are not public. The three-currency, three-industry model presented here is original to GEO Wiki. It is a practical framework for discussing investment with a CFO, not a benchmark that should be treated as an industry reference.

2. Three currencies of GEO value

A generative answer can create value for a brand in three distinct ways. Each corresponds to a different form of credit, as explained in Citation vs Mention vs Link, and requires a different primary KPI and time horizon.

• Citation Value is the value of being cited as a source from which an AI extracts information. It combines an authority signal that can compound over time with a more limited path to direct referral traffic. Citability explains how brands earn citations.
• Substituted Traffic Value is the value of a click that might have occurred in traditional search but is replaced by a zero-click answer that mentions or cites the brand. It can be measured as influence captured by the brand or revenue lost to the missing visit. Both perspectives matter.
• Brand Authority Value is the value of an unlinked mention that strengthens the model’s entity prior over time. It develops most slowly, is the hardest to measure, and is often the largest source of value in B2B. Brand Mentions explains the underlying mechanism.

The table maps the three currencies to the three forms of credit:

These currencies are not interchangeable. A brand with a high Citation Rate but weak entity recognition remains vulnerable because its referral path can change when a model updates. A brand with strong Brand Authority Value but no citations lacks a direct path to conversion. A defensible program invests in all three, with the balance determined by industry. Sections 4–6 quantify those differences.

3. Five proxy variables for estimating GEO ROI

Each model requires five variables: two from your analytics, two from a vendor tool, and one declared assumption.

1. Citation Rate: your share of cited answers for a topic
2. Answer Inclusion Rate: whether you appeared in an answer at all
3. Estimated answer volume: annual search volume × AI-search adoption rate
4. Estimated consideration lift: declared per mention or citation
5. Baseline conversion rate: measured on the equivalent SEO surface

Here is where each variable comes from:

• Variables 1 and 2 come from a GEO monitoring vendor such as Otterly, Ahrefs Brand Radar, Profound, BrightEdge, or Similarweb. The GEO Metrics vendor matrix compares their formulas and terminology.
• Variable 3 is the multiplier most often estimated inaccurately. Annual category search volume comes from a keyword tool, while the AI-search adoption rate comes from published market data. Relevant figures include Similarweb’s 357% year-over-year growth in referral visits, BrightEdge’s estimate that AI Overviews appear on about 48% of searches, and Gartner’s forecast of a 25% decline in traditional search volume by 2026. A defensible 2026 estimate is that 25%–50% of category queries encounter an AI-mediated answer.
• Variable 4 is the least certain. No published industry-wide figure exists for consideration lift per mention. State the assumption explicitly. Conservative defaults are 1.5%–2.5% for B2B SaaS and 0.5%–1% for B2C e-commerce.
• Variable 5 comes from your analytics. It provides the SEO baseline used in the substituted-traffic calculation.

Treat any value you cannot source as a declared assumption and present it as a range. The CFO brief in Section 10 provides a structure for doing so.

4. The B2B / SaaS ROI model: pipeline influence

Mentions tend to create the most value in B2B. A buyer may read an AI answer during the research phase without clicking, then become an inbound lead three to six months later through branded search, a direct visit, or a colleague’s referral. Forrester’s 2025 prediction reported that more than 90% of B2B buyers who used generative AI to inform purchases of at least $1 million described positive results, and that 89% of buyers had adopted generative AI by 2025. That activity may not appear in analytics when it happens, but it can emerge in the pipeline six months later.

Formula (proxy):

GEO Pipeline Value = Annual Category Search × AI-Search Adoption %
                   × Answer Inclusion Rate
                   × Consideration Lift per Mention
                   × Pipeline Conversion Rate
                   × Avg Contract Value

Worked example: Consider a SaaS company with an $80K average contract value in a category that receives 200K research-stage queries per year. Assume that 35% of those queries occur on AI surfaces, the company has an 18% Answer Inclusion Rate, consideration lift is treated as a declared range, and the end-to-end pipeline-to-closed-won rate is 14%.

The range is intentionally wide because uncertainty should remain visible. A $282K point estimate is not defensible on its own. A range from $72K in the low case to $1.08M in the high case, with $282K as the modeled midpoint, is more credible. Consideration lift is the least certain variable, so state it, justify it, and revisit it after one quarter of measurement.

This model does not work well for very-low-ACV B2B businesses, where variation in the estimate is large relative to the deal size; sales-led businesses with no inbound channel; or regulated industries in which AI presents an unpredictable mix of sources instead of consistently favoring authoritative ones. For implementation guidance, see GEO for SaaS / B2B.

5. The B2C / e-commerce ROI model: purchase influence

Citations tend to matter more in B2C. Product-comparison queries and searches for the “best X for Y” often produce listicles and reviews. An explicit citation, rather than a mention alone, can generate a measurable referral session, and the conversion usually occurs sooner than it does in a B2B pipeline.

Formula (proxy):

GEO Purchase Value = Annual Category Search × AI-Search Adoption %
                   × Citation Rate
                   × AI-Referred CTR
                   × Site Conversion Rate
                   × AOV × Repeat Multiplier

Worked example: Consider a beauty brand with an $85 average order value in a category that receives 4 million queries per year. Assume that 28% of those queries occur on AI surfaces and that the brand has a 6% Citation Rate. Set the AI-referred click-through rate within the bounds derived from Pew’s 8% link-in-summary figure and Ahrefs’ 34.5% suppression estimate for the top search result. Site conversion is 2.4%, and the repeat multiplier for lifetime gross revenue, including repeat purchases, is 1.8.

AI-referred click-through rate is the least certain variable. Pew provides a lower bound of about 1% for clicks on links within the summary. Ahrefs provides an upper bound based on the roughly 15% baseline click-through rate that the top search result would have received before suppression by an AI Overview. A realistic midpoint for a cited source in a commercial query lies somewhere between those figures. Using these published bounds is more defensible than adopting a vendor’s marketing estimate.

This model does not work well for commodity products dominated by marketplace listings. A query such as “best USB-C cable” may lead the AI to Amazon instead of a direct-to-consumer site. It also performs poorly for high-AOV, considered purchases, such as a $4,000 mattress or an $80K SaaS contract, where buying behavior resembles B2B more than B2C. For implementation guidance, see GEO for E-commerce.

6. The media / publisher ROI model: advertising, trust, and licensing

For publishers, the balance shifts, and the loss is easier to quantify. Every substituted click creates a measurable loss in the form of unearned advertising revenue, while potential gains from brand authority, direct visits, or licensing arrive later. Litigation and licensing activity from 2023 through 2025 reflects that imbalance:

Two formulas capture the losses and gains:

Lost   = Substituted Traffic × (RPM × pageviews_per_session)
Gained = (Citation Frequency × Authority Lift on Direct Visits)
       + (Brand-Search Lift × Direct-Visit Value)
       + (Optional: Licensing Revenue)

Worked example: Consider a mid-tier vertical publisher with 12 million monthly organic sessions, a 22% AIO impression rate on top-of-funnel queries, about 30% click suppression on affected queries, a $42 RPM, and 1.8 pageviews per session.

Monthly Lost (Substituted)  = 12M × 22% × 30% × ($42/1000 × 1.8)
                            ≈ 12M × 0.066 × $0.0756
                            ≈ $59,875 / month → ~$718K / year

For most publishers without a licensing agreement, this loss exceeds plausible gains from direct visits or branded search unless licensing revenue is included. Penske’s filing claims that AI Overviews appear on about 20% of searches linking to its properties and that affiliate revenue has fallen by more than one-third since late 2024. The lawsuit also signals the financial stakes. Most publishers without a licensing agreement cannot make AI search profitable on advertising revenue alone. For implementation guidance, see GEO for Media.

7. The cost side: implementing and operating GEO

ROI requires both value and cost. GEO expenses fall into three categories:

GEO Audit helps define the one-time work, while AI Citation Tracking defines the ongoing measurement process. In 2026, prices for GEO monitoring tools such as Profound, Otterly, Ahrefs Brand Radar, Conductor, and BrightEdge range from roughly $300 per month for a single-brand SMB plan to more than $5,000 per month for an enterprise plan covering multiple brands and competitors. Pricing changes quickly, so confirm any figure on the vendor’s website before presenting it to leadership.

Hidden costs are the most frequently underestimated. A schema rollout described as “two days of engineering” may consume a full week of an engineer’s time once review cycles, security checks, and redeployments are included. A content team assigned to authority pages also gives up part of its normal publishing schedule.

8. Time to value: realistic payback curves

GEO investment pays back in three distinct phases, each on a different schedule:

The phases occur in sequence because each depends on the previous one. Crawl access must come first. Citation lift requires content that engines can use to reground their answers. Brand authority depends on off-site signals that take several quarters to accumulate.

Returns can also decline when investment stops. Ahrefs’ study of 17 million citations found that AI-cited content is, on average, 25.7% fresher than organic Google results. Maintaining that level of freshness requires continued attention. Content Freshness explains how the freshness threshold works and why it may be lower than expected. Google documents freshness as a ranking system in its own right and describes “various ‘query deserves freshness’ systems designed to show fresher content for queries where it would be expected” in its ranking systems guide. The threshold therefore depends on the query rather than applying universally. The GEO Maturity Model presents the same progression by maturity stage instead of by month.

9. When GEO does not pay back

In five situations, the numbers may not justify substantial GEO investment beyond a low-cost technical foundation:

1. A very-low-volume niche. Annual answer volume may be too small for any of the three currencies to reach a measurable level. A B2B category with fewer than about 10,000 queries per year will struggle to rise above measurement noise.
2. Commodity products dominated by marketplace listings. Queries such as “best USB-C cable” or “cheapest paper towels” may lead the AI to Amazon, Wayfair, or Walmart. A direct-to-consumer source is unlikely to earn a citation, and marketplaces capture the substituted traffic.
3. Purely paid or fully product-led acquisition. A company that grows entirely through paid advertising or in-product viral loops has no inbound research funnel for GEO to influence. The model has no relevant conversion path through which to create value.
4. A relationship-driven B2B pipeline that is already saturated. In a category driven by long enterprise sales cycles, outbound sales, and expansion among existing customers, pipeline may be limited by capacity rather than visibility.
5. The pre-PMF stage. The product itself offers a higher marginal return. Building visibility infrastructure before the product has achieved product-market fit is premature optimization.

Deferring a larger GEO investment is reasonable in cases 1, 3, 4, and 5. The technical-foundation tier of the GEO Audit remains inexpensive enough to maintain readiness if demand in the category grows, but the broader content investment can wait.

Regulated industries such as healthcare, legal services, and finance do not fit neatly into these five cases, despite the assumption that AI systems favor authoritative sources. Empirical data points in the opposite direction: AI Overviews for health queries cite an unpredictable mix dominated by accessible content from YouTube and consumer sites rather than reliable medical sources. In regulated fields, GEO is therefore partly defensive because it can help prevent incorrect attribution involving the brand. The value is harder to model, but it is not absent. These industries need a separate model rather than a decision to skip GEO entirely.

10. Presenting the numbers to a CFO: a one-page brief

A one-page brief gives a CFO the assumptions, range, and investment decision in a form that is easy to evaluate:

GEO Investment Brief: [Company Name], [Quarter]

1. Question being answered
2. Scope (topics, engines, regions, and competitors)
3. Three-currency snapshot
   - Citation Value baseline
   - Substituted Traffic Value baseline
   - Brand Authority Value baseline
4. Pipeline and revenue model
   - Low, middle, and high projections
5. Requested investment and payback period
6. What we will not know until [milestone date]

Five rules for the brief itself:

1. Assign a number to every assumption. Do not say “we think it will grow” without specifying a percentage.
2. Show low, middle, and high estimates. CFOs tend to distrust point estimates more than ranges.
3. Separate what is measurable now from what will become measurable in six months. A clear boundary is more credible than an overstated promise.
4. Compare GEO with alternative investments. Paid advertising, content marketing, and an additional sales hire each have their own ROI range. GEO must compete with those choices rather than be evaluated in isolation.
5. End with the smallest defensible initial commitment. This is usually one quarter of technical foundation work plus a baseline measurement, not a multiyear request. Use first-quarter data to support any larger investment.

Three practices immediately undermine credibility:

• Do not lead with a single number such as “AI visibility score = 67/100.” Scores are not comparable across vendors because their formulas are opaque, as explained in Section 4 of GEO Metrics.
• Do not present the output of one vendor’s ROI calculator as a definitive answer when its formula is not public.
• Do not use Share of Voice alone as a proxy for ROI. Its denominator determines the result and can be manipulated easily.

Aggarwal et al. 2024 (arXiv:2311.09735) provides the academic basis for treating AI visibility as an investment. The paper coined GEO and found that content rewrites could increase answer visibility by up to 40%. If that improvement is real, the financial question is how much it is worth and which businesses benefit.

References

Macro adoption data:

• Gartner: Press Release: Search Engine Volume Will Drop 25% by 2026 (Feb 2024)
• BrightEdge: One Year of AI Overviews (May 2025)
• Similarweb: 2025 Generative AI Report (Dec 2025)
• Forrester: Predictions 2025: Younger Business Buyers and GenAI (Oct 2024)

Click-suppression evidence:

• Pew Research Center: Google users less likely to click with AI summaries (Jul 2025; Google disputes representativeness)
• Ahrefs: AI Overviews Reduce Clicks by 34.5% (Apr 2025; correlational)

Academic basis:

• Aggarwal, P. et al. (2024). GEO: Generative Engine Optimization. KDD ‘24. arXiv:2311.09735 · ACM DL · GEO Wiki summary

Publisher / licensing landscape:

• Axios: AP × OpenAI licensing deal (Jul 2023)
• Washington Post: NYT v OpenAI lawsuit (Dec 2023)
• OpenAI: Axel Springer partnership (Dec 2023) · Reddit partnership (May 2024)
• Google: Reddit partnership (Feb 2024)
• The Bookseller: Wiley AI deals total $44M (Sep 2024)
• TechCrunch: Penske v Google AIO suit (Sep 2025)

Freshness and operational context:

• Ahrefs: New Study: AI Assistants Prefer to Cite ‘Fresher’ Content (Jul 2025) · Fresh Content: Why Publish Dates Make or Break Rankings (Mar 2026)
• Google Search Central: AI features and your website (Dec 2025) · A Guide to Google Search Ranking Systems (Dec 2025)

Vendor formula references:

• Otterly.ai: Brand Report KPI Definitions
• Ahrefs: Brand Radar Methodology